SSGCN: a graph convolutional neural network model based on syntactic structure and similarity features for relation extraction in shipbuilding industry

Chuanyang Bai; Jiaming Zheng; Jiexin Hu; Linna Zhou; Jiabin Chen; Hongjie Yang

doi:10.1117/12.3014256

8 December 2023 SSGCN: a graph convolutional neural network model based on syntactic structure and similarity features for relation extraction in shipbuilding industry

Chuanyang Bai, Jiaming Zheng, Jiexin Hu, Linna Zhou, Jiabin Chen, Hongjie Yang

Author Affiliations +

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430D (2023) https://doi.org/10.1117/12.3014256
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

The construction of the standard knowledge graph is to realize the reorganization of the internal knowledge of the traditional standard texts and achieve the purpose of innovating knowledge storage and the practice of knowledge supply. Technical requirement extraction is a typical relation extraction task in the construction process of a standard knowledge graph. However, the existing relation extraction models can not achieve ideal performance due to standard texts' unique organizational forms and special writing characteristics. Therefore, This paper proposes a Graph Convolutional Neural Network model based on Syntactic structure and Similarity features (SSGCN), integrating expert knowledge in syntactic pruning, dynamically interfering with the weight matrix by the pruning strategy based on attention, and making full use of supervised relation label semantic similarity features. The experiment in this paper compares the large language models (LLMs) such as GPT-3, and our model achieves better experimental performance than other relation extraction models on specialized datasets in the field, providing a related solution for standard technical requirement extraction tasks.

Citation Download Citation

Chuanyang Bai, Jiaming Zheng, Jiexin Hu, Linna Zhou, Jiabin Chen, and Hongjie Yang "SSGCN: a graph convolutional neural network model based on syntactic structure and similarity features for relation extraction in shipbuilding industry", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430D (8 December 2023); https://doi.org/10.1117/12.3014256

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available